Frequency spectrum analyzers have been used for many years to transform a time domain signal of unknown frequency characteristics into a frequency spectrum output such that the frequency components of the incoming signal can be determined. Typically a system performs a Fourier transform on the time domain signal to translate that signal into a frequency spectrum output. First the apparatus samples the incoming time domain signal for a predetermined number of intervals and stores each sample of the input signal in a storage device or memory. The apparatus then performs a Fourier transform on these stored samples of the incoming signal and at the end of this processing interval an output in frequency spectrum form is generated. After each processing cycle is completed the spectrum analyzer again samples the incoming signal, stores that data, and then performs the Fourier transform on the stored signal data.
The difficulty with this arrangement is that the incoming signal must be disregarded for a period of time following each period of signal sampling to allow time for processing the stored samples of the input signal. During the processing interval the incoming signal must be disregarded even though it may have radically changed in frequency content. These devices cannot properly be characterized as real time frequency spectrum analyzers since there are gaps between signal sampling periods to allow processing of the stored signal.
Other problems associated with prior art frequency spectrum analyzers are that they are large, bulky and expensive, often utilizing standard large capacity digital computers to process the information. In addition these devices consume large amounts of power.